Malware Squid: A Novel IoT Malware Traffic Analysis Framework using Convolutional Neural Network and Binary Visualisation: Preprint

R Shire; S Shiaeles; K Bendiab; B Ghita; N Kolokotronis

doi:10.48550/arxiv.2109.03375

Malware Squid: A Novel IoT Malware Traffic Analysis Framework using Convolutional Neural Network and Binary Visualisation: Preprint

R Shire
, S Shiaeles
, K Bendiab
, B Ghita
, N Kolokotronis

School of Engineering, Computing and Mathematics

Research output: Working paper / Preprint › Preprint

43 Downloads (Pure)

Original language	English
Number of pages	0
DOIs	https://doi.org/10.48550/arxiv.2109.03375
Publication status	Published - 7 Sept 2021

Keywords

4605 Data Management and Data Science
4606 Distributed Computing and Systems Software
46 Information and Computing Sciences
4604 Cybersecurity and Privacy

Access to Document

10.48550/arxiv.2109.03375

2109

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keywords = "4605 Data Management and Data Science, 4606 Distributed Computing and Systems Software, 46 Information and Computing Sciences, 4604 Cybersecurity and Privacy",

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AU - Shiaeles, S

AU - Bendiab, K

AU - Ghita, B

AU - Kolokotronis, N

PY - 2021/9/7

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KW - 4605 Data Management and Data Science

KW - 4606 Distributed Computing and Systems Software

KW - 46 Information and Computing Sciences

KW - 4604 Cybersecurity and Privacy

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BT - Malware Squid: A Novel IoT Malware Traffic Analysis Framework using Convolutional Neural Network and Binary Visualisation

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Malware Squid: A Novel IoT Malware Traffic Analysis Framework using Convolutional Neural Network and Binary Visualisation: Preprint

Keywords

Access to Document

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Malware Squid: A Novel IoT Malware Traffic Analysis Framework Using Convolutional Neural Network and Binary Visualisation

Cite this